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Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semi-supervised ensemble method named UDEED is proposed...
In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels. Most existing algorithms solve MIML problem via the intuitive way of identifying its equivalence in degenerated version of MIML. However, this identification process may lose useful information encoded in training examples and therefore be harmful...
In multi-instance learning, each example is represented by a bag of instances while associated with a binary label. Under standard multi-instance learning settings, one example is labeled as a positive bag if at least one of its instances is positive. Otherwise, it is labeled as a negative bag. Although based on the above assumption, standard multi-instance learning has achieved much success in solving...
Multi-instance multi-label learning (MIML) deals with the problem where each training example is associated with not only multiple instances but also multiple class labels. Previous MIML algorithms work by identifying its equivalence in degenerated versions of multi-instance multi-label learning. However, useful information encoded in training examples may get lost during the identification process...
One of the basic characteristics in human problem solving is the ability to conceptualize the world at different granularities and translate from one abstraction level to the others easily. But so far computers can only deal with one abstraction level in problem solving generally. It seems important to develop new techniques which will in some way enable the computers to represent the world at different...
In this paper, a new short-term traffic flow prediction model and method based on incremental support vector regression (ISVR) is proposed, according to the data collected sequentially by the probe vehicle or loop detectors, which can update the prediction function in real time via incremental learning way. As a result, it is fitter for the real engineering application. The ISVR model was tested by...
The theory and applications of artificial neural networks have developed rapidly since the mathematical model of neuron was presented, but the design of network structure for a certain problem was a roadblock over a long period of time. In 1990s, the covering algorithm for forward neural network was put forward, this algorithm is a constructive machine learning method, it designs network with sphere...
The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming...
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